Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
#install.packages("gganimate")
#install.packages("gifski")
#install.packages("av")
#install.packages("gapminder")
library(tidyverse)
library(gganimate)
library(gifski)
#library(av)
library(gapminder)
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
gapminder <- gapminder
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point()+
scale_x_log10()+
ggtitle("gdpPercap against lifeExp, x-axis on log10 scale")
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point()+
ggtitle("gdpPercap against lifeExp, x-axis in regular units")
We see an interesting spread with an outlier to the right. Answer the following questions, please:
The first plot shows the plot with the x-axis on a log10 scale, whereas the second shows the x-axis in regular units. Using a log10 scale on the x-axis makes the spread of the datapoints far more easy to see.
# adding text labels for each country to see what country the outlier is
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, label = country)) +
geom_point()+
scale_x_log10()+
geom_text() # geom_text() adds text labels to the geom-points by the defined label in the aes-argument (here country)
Kuwait is the outlier
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
We now have more countries with a higher gdpPercap, and also more countries with a higher life expectancy.
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
options(scipen = 100) # removes the scientific notation in this R-session
# the color argument defines what to color by - here by continent
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10()+
# adding x and y labels
xlab("GDP per capita, log10 scaled")+
ylab("Life expectancy in years")+
# change legend names
labs(size = "Population", color = "Continent")
gapminder %>%
filter(year == 2007) %>%
arrange(desc(gdpPercap)) # sorting the countries by GDP per capita
## # A tibble: 142 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 2007 80.2 4627926 49357.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Singapore Asia 2007 80.0 4553009 47143.
## 4 United States Americas 2007 78.2 301139947 42952.
## 5 Ireland Europe 2007 78.9 4109086 40676.
## 6 Hong Kong, China Asia 2007 82.2 6980412 39725.
## 7 Switzerland Europe 2007 81.7 7554661 37506.
## 8 Netherlands Europe 2007 79.8 16570613 36798.
## 9 Canada Americas 2007 80.7 33390141 36319.
## 10 Iceland Europe 2007 81.8 301931 36181.
## # … with 132 more rows
If we look at the first five rows, we can see the five riches countries, which are Norway, Kuwait, Singapore, United States and Ireland.
The comparison would be easier if we had the two graphs together,
animated. We have a lovely tool in R to do this: the
gganimate package. Beware that there may be other packages
your operating system needs in order to glue interim images into an
animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
anim
…
This plot collates all the points across time. The next step is to
split it into years and animate it. This may take some time, depending
on the processing power of your computer (and other things you are
asking it to do). Beware that the animation might appear in the bottom
right ‘Viewer’ pane, not in this rmd preview. You need to
knit the document to get the visual inside an html
file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim + transition_time(year)
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may
need to troubleshoot your installation of gganimate and
other packages
transition_states() and transition_time()
functions respectively)# using transition_states
anim + transition_states(year,
transition_length = 1,
state_length = 1) +
labs(title = 'Year: {closest_state}')
A solution was found in this StackOverflow post: https://stackoverflow.com/questions/37397303/change-label-of-gganimate-frame-title
The year will be given by the closest state, meaning that when gganimate is transitioning between between states (i.e., years), it will display the closest state, i.e., the current year, between transitioning.
# using transition_time (more smooth movement)
anim + transition_time(year)+
labs(title = 'Year: {frame_time}')
The equivalent is done for the transition_time solution, however this time using “frame_time” as the title. As explained here, https://gganimate.com/reference/transition_time.html, frame_time tells you what time, here year, the current frame is on, and can thus be used as a title for the animated plot.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10()+
xlab("GDP per capita, log10 scaled")+
ylab("Life expectancy in years")+
# change legend names
labs(size = "Population", color = "Continent")+
transition_states(year,
transition_length = 1,
state_length = 1) +
labs(title = 'Year: {closest_state}')
anim2
gapminder_unfiltered dataset and
download more at https://www.gapminder.org/data/ ]I would like to examine the development of population sizes in Asian countries across the years:
population_plot <- ggplot(subset(gapminder, continent == "Asia"), aes(x = country, y = pop, label = country, color = country))+
geom_point()+
geom_text(position=position_jitter(width = 0.1, height = 0.1))+ # the position_jitter ensures that all the text labels are not on top of each other. however, adding too much jitter makes it difficult to see which text label belongs to what point.
scale_y_log10()+
theme(legend.position="none")+ # removes the legend, as there are a lot of countries and it does not look great with all of them in a legend
scale_x_discrete(labels = NULL, breaks = NULL) + # removes the x-axis ticks
labs(x = "", y = "Population size, log 10 transformed") # removes the x-axis label and changes the y-axis label
population_plot + transition_states(year,
transition_length = 1,
state_length = 1) +
labs(title = 'Population sizes in Asian countries in {closest_state}')
This visualization answers my questions since I have subsetted the gapminder data to only look at the Asian countries in the data. Next, I have used the “geom_point()” function to visualize each Asian country as a point. In the “aes” variable, I have specified that the text-label on each point should be labeled by country, and have also given each country a color through the “color = country” argument. The “geom_text” function adds the text label of each country to the points.
The visualization looks at the development through the years by using gganimate and using each year as a state in the animation.